The present disclosure generally relates to the field of prediction analysis and building predictive models for applications, and more specifically to a system and method for building a random walk model with heterogeneous graphs to leverage multiple source data for prediction tasks.
Many predictive analytics problems involve a variety of different types of data. For example, in building a predictive model to generate sales recommendations, one might consider both the relationships between clients and the attributes of clients' industry and the characteristics of the products. In another example of legislative prediction, where the goal is to predict the votes from legislators on future bills, both political and social relationships between legislators and the semantic description of bills are available for predictive analysis. Besides predicting who might buy which product, or who might vote yea/nay on a bill, estimating the influence of entities (e.g., clients or legislators) is also very important for decision support.
Most of the existing learning methods rely on feature-based data representation by vectors in a predefined metric space, which lacks the capacity to handle relational information. Emerging techniques for social network analysis, such as relational learning, usually focus on homogenous relational links of single type of samples. To apply these methods for analyzing such complex heterogeneous data, one has to simplify and degrade the heterogeneous sources, such as summing multiple relation links in a heuristic way, which often results in information loss and unsatisfactory prediction accuracy. In addition, many important problems, such as estimating the influencers, do not even fit into this simplification scheme and far less advances have been made for complex data represented in such a heterogeneous way.
Therefore, there exists emerging need to develop tools and models to incorporate such heterogeneous data to: 1) perform efficient prediction or recommendation, and 2) identify the most influential entities.